Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: Emerging machine learning techniques and future avenues

被引:24
作者
La Rosa, Francesco [1 ,2 ,3 ]
Wynen, Maxence [2 ,4 ,5 ,6 ,7 ]
Al-Louzi, Omar [8 ,9 ]
Beck, Erin S. [3 ,8 ]
Huelnhagen, Till [1 ,6 ,7 ,10 ]
Maggi, Pietro [5 ,11 ,12 ]
Thiran, Jean-Philippe [1 ,2 ,6 ,7 ]
Kober, Tobias [1 ,6 ,7 ]
Shinohara, Russell T. [13 ,14 ,15 ]
Sati, Pascal [8 ,9 ]
Reich, Daniel S. [8 ]
Granziera, Cristina [16 ,17 ,18 ]
Absinta, Martina [19 ,20 ,21 ]
Cuadra, Meritxell Bach [2 ,6 ,7 ]
机构
[1] Ecole Polytech Fed Lausanne EPFL, Signal Proc Lab, Lausanne, Switzerland
[2] CIBM Ctr Biomed Imaging, Lausanne, Switzerland
[3] Icahn Sch Med Mt Sinai, Dept Neurol, New York, NY 10029 USA
[4] UCLouvain, ICTeam, Louvain La Neuve, Belgium
[5] UCLouvain, Inst Neurosci IoNS, Louvain Inflammat Imaging Lab NIL, Brussels, Belgium
[6] Lausanne Univ, Dept Radiol, Lausanne, Switzerland
[7] Lausanne Univ Hosp, Lausanne, Switzerland
[8] NINDS, NIH, Translat Neuroradiol Sect, Bethesda, MD USA
[9] Cedars Sinai Med Ctr, Dept Neurol, Los Angeles, CA USA
[10] Siemens Healthcare AG, Adv Clin Imaging Technol, Lausanne, Switzerland
[11] Catholic Univ Louvain, Clin Univ St Luc, Dept Neurol, Brussels, Belgium
[12] CHU Vaudois, Dept Neurol, Lausanne, Switzerland
[13] Univ Penn, Dept Radiol, Ctr Biomed Image Comp & Anal CBICA, Philadelphia, PA USA
[14] Univ Penn, Ctr Clin Epidemiol & Biostat, Penn Stat Imaging & Visualizat Endeavor PennSIVE, Philadelphia, PA USA
[15] Univ Penn, Perelman Sch Med, Dept Biostat Epidemiol & Informat, Philadelphia, PA USA
[16] Univ Basel Hosp, Dept Biomed Engn, Translat Imaging Neurol ThINk Basel, Fac Med, Basel, Switzerland
[17] Univ Basel, Basel, Switzerland
[18] Univ Basel Hosp, MS Ctr & Res Ctr Clin Neuroimmunol & Neuroscience, Neurol Clin & Policlin, Basel, Switzerland
[19] IRCCS San Raffaele Hosp, Milan, Italy
[20] Univ Vita Salute San Raffaele, Milan, Italy
[21] Johns Hopkins Univ, Sch Med, Dept Neurol, Baltimore, MD USA
基金
美国国家卫生研究院; 瑞士国家科学基金会;
关键词
WHITE-MATTER LESIONS; CLINICAL DISABILITY; FLAIR-ASTERISK; MS LESIONS; BRAIN MRI; SEGMENTATION; DIAGNOSIS; MISDIAGNOSIS; PROGRESSION; MORPHOLOGY;
D O I
10.1016/j.nicl.2022.103205
中图分类号
R445 [影像诊断学];
学科分类号
100207 ;
摘要
The current diagnostic criteria for multiple sclerosis (MS) lack specificity, and this may lead to misdiagnosis, which remains an issue in present-day clinical practice. In addition, conventional biomarkers only moderately correlate with MS disease progression. Recently, some MS lesional imaging biomarkers such as cortical lesions (CL), the central vein sign (CVS), and paramagnetic rim lesions (PRL), visible in specialized magnetic resonance imaging (MRI) sequences, have shown higher specificity in differential diagnosis. Moreover, studies have shown that CL and PRL are potential prognostic biomarkers, the former correlating with cognitive impairments and the latter with early disability progression. As machine learning-based methods have achieved extraordinary performance in the assessment of conventional imaging biomarkers, such as white matter lesion segmentation, several automated or semi-automated methods have been proposed as well for CL, PRL, and CVS. In the present review, we first introduce these MS biomarkers and their imaging methods. Subsequently, we describe the corresponding machine learning-based methods that were proposed to tackle these clinical questions, putting them into context with respect to the challenges they are facing, including nonstandardized MRI protocols, limited datasets, and moderate inter-rater variability. We conclude by presenting the current limitations that prevent their broader deployment and suggesting future research directions.
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页数:14
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